We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.

This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.

We enable a robot to rapidly and autonomously specialize parameterized skills by planning to practice them. The robot decides what skills to practice and how to practice them. The robot is left alone for hours, repeatedly practicing and improving.

We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.

We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.

We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.

We formulate compositional generalization in object-oriented world modeling, and propose a soft and efficient mechanism for practice.

We learn latent representations enforced with known transformation laws (group action), and apply this idea on equivariant latent world modeling.

We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space.